Evidence map›Paper›PMID 32462887›Full record

ArticleThe British journal of radiology2020

A comparison of manually populated radiology information system digital radiographic data with electronic dose management systems.

Nathan Dickinson, Matthew Dunn

Abstract readComparative StudyMulticenter Study
In one paragraph

Article in The British journal of radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Nathan DickinsonMedical Physics and Clinical Engineering, Nottingham University Hospitals, Nottingham, NG5 1PB, United Kingdom.
Matthew DunnMedical Physics and Clinical Engineering, Nottingham University Hospitals, Nottingham, NG5 1PB, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo assess the accuracy and agreement of radiology information system (RIS) kerma-area product (KAP) data with respect to automatically populated dose management system (DMS) data for digital radiography (DR).

methodsAll adult radiographic examinations over 12 months were exported from the RIS and DMS at three centres. Examinations were matched by unique identifier fields, and grouped by examination type. Each centre's RIS sample completeness was calculated, as was the percentage of the RIS examination KAP values within 5% of their DMS counterparts (used as an accuracy metric). For each centre, the percentage agreement between the RIS and DMS examination median KAP values was computed using a Bland-Altman analysis. At two centres, up to 42.5% of the RIS KAP units entries were blank or invalid; corrections were attempted to improve data quality in these cases.

resultsStatistically significant intersite variation was seen in RIS data accuracy and the agreement between the uncorrected RIS and DMS median KAP data, with a Bland-Altman bias of up to 11.1% (with a -31.7% to 53.9% 95% confidence interval) at one centre. Attempts to correct invalid KAP units increased accuracy but produced worse agreement at one centre, a slight improvement at another and no significant change in the third.

conclusionThe RIS data poorly represented the DMS data. ADVANCES IN KNOWLEDGE: RIS KAP data are a poor surrogate for DMS data in DR. RIS data should only be used in patient dose surveys with an understanding of its limitations and potential inaccuracies.

Indexed as

AdultBiasData CollectionHumansRadiation DosageRadiation ProtectionRadiographic Image EnhancementRadiology Information SystemsReference StandardsSensitivity and Specificity

Identifiers

PMID32462887
PMCPMC7336068

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.